{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "84ea27ff",
   "metadata": {},
   "source": [
    "## 0. Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4d3bfc90",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pytorch_lightning as pl\n",
    "import torch\n",
    "from scipy.stats import norm\n",
    "from torch import nn\n",
    "from torch.distributions import Normal\n",
    "from torch.optim import Adam\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "51173c7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 0\n"
     ]
    }
   ],
   "source": [
    "seed = 0\n",
    "pl.seed_everything(seed=seed);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00250567",
   "metadata": {},
   "source": [
    "## 1. Model and Data Modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a8014977",
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConvexNN(nn.Module):\n",
    "    def __init__(self, input_dim=1, hidden_dim=128, output_dim=1, max_out=4, bias=False):\n",
    "        super().__init__()\n",
    "        max_out_adusted_hidden_dim = hidden_dim // max_out\n",
    "        self.input_to_hidden = nn.Sequential(\n",
    "            nn.Linear(input_dim, hidden_dim, bias=bias),\n",
    "            nn.MaxPool1d(kernel_size=max_out)\n",
    "        )\n",
    "        self.main = nn.Sequential(\n",
    "            nn.Linear(max_out_adusted_hidden_dim, hidden_dim, bias=bias),\n",
    "            nn.MaxPool1d(kernel_size=max_out),\n",
    "            nn.Linear(max_out_adusted_hidden_dim, output_dim, bias=bias)\n",
    "        )\n",
    "        self.project_params_for_cvxty()\n",
    "    \n",
    "    def forward(self, x):\n",
    "        hidden = self.input_to_hidden(x)\n",
    "        return self.main(hidden)\n",
    "    \n",
    "    def project_params_for_cvxty(self):\n",
    "        for module in self.main.modules():\n",
    "            if module and hasattr(module, 'weight'):\n",
    "                with torch.no_grad():\n",
    "                    module.weight.data.relu_()\n",
    "                    \n",
    "    def test_convexity(self, batch_size=64, eps=1e-6):\n",
    "        x = torch.randn((batch_size, 1))\n",
    "        with torch.no_grad(): \n",
    "            # Sample lambda uniformly\n",
    "            lmbds = torch.rand((batch_size, 1))\n",
    "\n",
    "            # Shuffle indices to randomly sample points\n",
    "            indices1 = torch.randperm(x.shape[0])\n",
    "            indices2 = torch.randperm(x.shape[0])\n",
    "\n",
    "            # Check that `f(lambda x + (1-lambda)y) <= lambda f(x) + (1-lambda) f(y)`\n",
    "            convex_combo_of_inputs = self(lmbds * x[indices1] + (1 - lmbds) * x[indices2])\n",
    "            convex_combo_of_outputs = lmbds * self(x[indices1]) + (1 - lmbds) * self(x[indices2])\n",
    "            return torch.sum(convex_combo_of_inputs <= convex_combo_of_outputs + eps).item() / batch_size\n",
    "        \n",
    "    def get_weight_norms(self):\n",
    "        param_norm = 0\n",
    "        numel = 0\n",
    "        for name, module in self.named_modules():\n",
    "            param_norm = 0\n",
    "            numel = 0\n",
    "            if name == '':\n",
    "                continue\n",
    "            for param in module.parameters():\n",
    "                if param.requires_grad:\n",
    "                    with torch.no_grad():\n",
    "                        param_norm += param.norm(2).detach().item() ** 2\n",
    "                        numel += param.numel()\n",
    "        return math.sqrt(param_norm) / numel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d10ff5be",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiModalGaussian1D:\n",
    "    def __init__(self, n_modes=3, mu_scale=1, sigma_scale=1, mus=None, sigmas=None):\n",
    "        super(MultiModalGaussian1D).__init__()\n",
    "        self.n_modes = n_modes\n",
    "        self.mus = mus if mus is not None else (-2*torch.rand(n_modes) + 1)*mu_scale\n",
    "        self.sigmas = sigmas if sigmas is not None else torch.rand(n_modes)*sigma_scale\n",
    "        self.dists = [Normal(m, s) for m, s in zip(self.mus, self.sigmas)]\n",
    "\n",
    "    def sample(self, batch_size):\n",
    "        return torch.concat([d.sample((batch_size // self.n_modes, 1)) for d in self.dists], dim=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19ba7e61",
   "metadata": {},
   "source": [
    "## 2. Optimization Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "64c3a225",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample(batch_size, dists):\n",
    "    return {k: d.sample(batch_size) for k, d in dists.items()}\n",
    "\n",
    "def optimization_loop(u, u_opt, dists, iterations, batch_size):    \n",
    "    pbar = tqdm(range(iterations), desc='Iter')\n",
    "    vdc_log = []\n",
    "    steps = 0\n",
    "    u.train()\n",
    "    for i in pbar:\n",
    "        # Sample pts\n",
    "        samples = sample(batch_size, dists)\n",
    "        \n",
    "        # Calculate objectives\n",
    "        critic_on_dominating = u(samples['μ_plus']).mean()\n",
    "        critic_on_dominated = u(samples['μ_minus']).mean()\n",
    "        vdc = critic_on_dominating - critic_on_dominated\n",
    "        vdc_log.append(vdc.item())\n",
    "        \n",
    "        # Take gradient step\n",
    "        u_opt.zero_grad()\n",
    "        vdc.backward()\n",
    "        u_opt.step()\n",
    "        u.project_params_for_cvxty()\n",
    "        steps += 1\n",
    "        \n",
    "        # Test u convexity\n",
    "        cvxty = u.test_convexity(batch_size=batch_size)\n",
    "        \n",
    "        # Log values to pbar\n",
    "        with torch.no_grad():\n",
    "            postfix_str = f'u(μ+): {critic_on_dominating.item():0.2f}, ' + \\\n",
    "                          f'u(μ-): {critic_on_dominated.item():0.2f}, ' + \\\n",
    "                          f'VDC: {vdc.item():0.2f}, ' + \\\n",
    "                          f'cvx: {cvxty:0.2f}'\n",
    "            pbar.set_postfix_str(postfix_str)\n",
    "    return {'vdc': vdc.item(), 'vdc_log': vdc_log}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0b0aa7d",
   "metadata": {},
   "source": [
    "## 3. Run optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7b48317a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparams\n",
    "u_hidden_dim = 32\n",
    "u_max_out = 4\n",
    "u_bias = True\n",
    "bsz = 1024\n",
    "iters = 5000\n",
    "u_lr = 5e-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5e627ad6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Models and optimizer\n",
    "u = ConvexNN(hidden_dim=u_hidden_dim, max_out=u_max_out, bias=u_bias)\n",
    "u_opt = Adam(params=u.parameters(), lr=u_lr, maximize=False)\n",
    "\n",
    "# Data\n",
    "mu3 = torch.FloatTensor([-10, 0, 10])\n",
    "sigma3 = torch.FloatTensor([2, 2, 2])\n",
    "μ_plus = MultiModalGaussian1D(n_modes=3, mus=mu3, sigmas=sigma3)\n",
    "\n",
    "mu1 = torch.zeros(1)\n",
    "sigma1 = torch.ones(1)*3\n",
    "μ_minus = MultiModalGaussian1D(n_modes=1, mus=mu1, sigmas=sigma1)\n",
    "dists = {'μ_plus': μ_plus, 'μ_minus': μ_minus}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "231d4501",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2b5b9e6977be42cc91cef6e4047a6a52",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Iter:   0%|          | 0/5000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run loop\n",
    "opt_output = optimization_loop(u=u, u_opt=u_opt, dists=dists, iterations=iters, batch_size=bsz);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f806a2e5",
   "metadata": {},
   "source": [
    "## 4. Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9399ead9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mix_norm_cdf(x, dist):\n",
    "    mcdf = 0.0\n",
    "    for i in range(dist.mus.shape[0]):\n",
    "        mcdf += 1/dist.mus.shape[0] * norm.cdf(x, loc=dist.mus[i].item(), scale=dist.sigmas[i].item())\n",
    "    return mcdf\n",
    "\n",
    "def mix_norm_pdf(x, dist):\n",
    "    mpdf = 0.0\n",
    "    for i in range(dist.mus.shape[0]):\n",
    "        mpdf += 1/dist.mus.shape[0] * norm.pdf(x, loc=dist.mus[i].item(), scale=dist.sigmas[i].item())\n",
    "    return mpdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b38d77f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pts = np.linspace(-15, 15, 100)\n",
    "fig, ax = plt.subplots(1, 3, figsize=(12,4), tight_layout=True)\n",
    "\n",
    "# PDF:\n",
    "pdf_values_μ_plus = np.array([mix_norm_pdf(c, dists['μ_plus']) for c in pts])\n",
    "pdf_values_μ_minus = np.array([mix_norm_pdf(c, dists['μ_minus']) for c in pts])\n",
    "ax[0].plot(pts, pdf_values_μ_plus, color='blue', label=r'$\\mu_+$')\n",
    "ax[0].plot(pts, pdf_values_μ_minus, color='red', label=r'$\\mu_-$')\n",
    "ax[0].set_title('PDF' + '\\n' + r'$\\mu_+ \\mathrm{(dominating)}, ~ \\mu_- \\mathrm{(dominated)}$')\n",
    "ax[0].set_xlabel(r'$x$')\n",
    "ax[0].set_ylabel('Density')\n",
    "ax[0].legend()\n",
    "\n",
    "\n",
    "# Survival fxn:\n",
    "cdf_values_μ_plus = np.array([mix_norm_cdf(c, dists['μ_plus']) for c in pts])\n",
    "cdf_values_μ_minus = np.array([mix_norm_cdf(c, dists['μ_minus']) for c in pts])\n",
    "ax[1].plot(pts, 1-cdf_values_μ_plus, color='blue', label='$\\mu_+$')\n",
    "ax[1].plot(pts, 1-cdf_values_μ_minus, color='red', label='$\\mu_-$')\n",
    "ax[1].set_title('Survival function ' + '$(1 - \\mathrm{CDF})$' + '\\n' + \\\n",
    "                '$\\mu_+ \\mathrm{(dominating)}, ~ \\mu_- \\mathrm{(dominated)}$')\n",
    "ax[1].set_xlabel('$x$')\n",
    "ax[1].legend()\n",
    "\n",
    "# VDC\n",
    "ax[2].plot(range(len(opt_output['vdc_log'])), opt_output['vdc_log'])\n",
    "ax[2].set_title('VDC Progression During Training\\n' + \\\n",
    "                '$\\mathrm{VDC}(\\mu_+, \\mu_-) = \\sup_{u \\in cvx}\\mathbb{E}_{\\mu_-}[u] - \\mathbb{E}_{\\mu_+}[u]$')\n",
    "ax[2].set_xlabel('Step' + '\\n' + \\\n",
    "                 '$\\mathrm{VDC}(\\mu_+, \\mu_-) = 0$' + \\\n",
    "                 r'$\\mathrm{~iff~} \\mathbb{E}_{\\mu_-}[u] \\leq \\mathbb{E}_{\\mu_+}[u], ~ \\forall u \\in cvx$')\n",
    "ax[2].set_ylabel('VDC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9df39408",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Uncomment below to generate separate plots\n",
    "\n",
    "# # PDF:\n",
    "# plt.plot(pts, pdf_values_μ_plus, color='blue', label=r'$\\mu_+$')\n",
    "# plt.plot(pts, pdf_values_μ_minus, color='red', label=r'$\\mu_-$')\n",
    "# plt.title('PDF' + '\\n' + r'$\\mu_+ \\mathrm{(dominating)}, ~ \\mu_- \\mathrm{(dominated)}$')\n",
    "# plt.xlabel(r'$x$')\n",
    "# plt.ylabel('Density')\n",
    "# plt.legend()\n",
    "# plt.show()\n",
    "\n",
    "# # Survival fxn:\n",
    "# cdf_values_μ_plus = np.array([mix_norm_cdf(c, dists[0]) for c in pts])\n",
    "# cdf_values_μ_minus = np.array([mix_norm_cdf(c, dists[1]) for c in pts])\n",
    "# plt.plot(pts, 1-cdf_values_μ_plus, color='blue', label='$\\mu_+$')\n",
    "# plt.plot(pts, 1-cdf_values_μ_minus, color='red', label='$\\mu_-$')\n",
    "# plt.title('Survival function ' + '$(1 - \\mathrm{CDF})$' + '\\n' + \\\n",
    "#                 '$\\mu_+ \\mathrm{(dominating)}, ~ \\mu_- \\mathrm{(dominated)}$')\n",
    "# plt.xlabel('$x$')\n",
    "# plt.legend()\n",
    "# plt.show()\n",
    "\n",
    "# # VDC\n",
    "# plt.plot(range(len(opt_output['vdc_log'])), opt_output['vdc_log'])\n",
    "# plt.title('VDC Progression During Training\\n' + \\\n",
    "#                 '$\\mathrm{VDC}(\\mu_+, \\mu_-) = \\sup_{u \\in cvx}\\mathbb{E}_{\\mu_-}[u] - \\mathbb{E}_{\\mu_+}[u]$')\n",
    "# plt.xlabel('Step' + '\\n' + \\\n",
    "#                  '$\\mathrm{VDC}(\\mu_+, \\mu_-) = 0$' + \\\n",
    "#                  r'$\\mathrm{~iff~} \\mathbb{E}_{\\mu_-}[u] \\leq \\mathbb{E}_{\\mu_+}[u], ~ \\forall u \\in cvx$')\n",
    "# plt.ylabel('VDC')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e4501c3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
